Abstract

Robot Interaction has always been a challenge in collaborative robotics. In tasks comprising Inter-Robot Interaction, robot detection is very often needed. We explore humanoid robots detection because, humanoid robots can be useful in many scenarios, and everything from helping elderly people live in their own homes to responding to disasters. Cameras are chosen because they are reach and cheap sensors, and there are lots of mature two-dimensional (2D) and 3D computer vision libraries which facilitate Image analysis. To tackle humanoid robot detection effectively, we collected a data set of various humanoid robots with different sizes in different environments. Afterward, we tested the well-known cascade classifier in combination with several image descriptors like Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set. Among the feature sets, Haar-like has the highest accuracy, LBP the highest recall, and HOG the highest precision. Considering Inter-Robot Interaction, it is evident that false positives are less troublesome than false negatives, thus LBP is more useful than the others.

title = "Inter-humanoid robot interaction with emphasis on detection: a comparison study",

abstract = "Robot Interaction has always been a challenge in collaborative robotics. In tasks comprising Inter-Robot Interaction, robot detection is very often needed. We explore humanoid robots detection because, humanoid robots can be useful in many scenarios, and everything from helping elderly people live in their own homes to responding to disasters. Cameras are chosen because they are reach and cheap sensors, and there are lots of mature two-dimensional (2D) and 3D computer vision libraries which facilitate Image analysis. To tackle humanoid robot detection effectively, we collected a data set of various humanoid robots with different sizes in different environments. Afterward, we tested the well-known cascade classifier in combination with several image descriptors like Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set. Among the feature sets, Haar-like has the highest accuracy, LBP the highest recall, and HOG the highest precision. Considering Inter-Robot Interaction, it is evident that false positives are less troublesome than false negatives, thus LBP is more useful than the others.",

N2 - Robot Interaction has always been a challenge in collaborative robotics. In tasks comprising Inter-Robot Interaction, robot detection is very often needed. We explore humanoid robots detection because, humanoid robots can be useful in many scenarios, and everything from helping elderly people live in their own homes to responding to disasters. Cameras are chosen because they are reach and cheap sensors, and there are lots of mature two-dimensional (2D) and 3D computer vision libraries which facilitate Image analysis. To tackle humanoid robot detection effectively, we collected a data set of various humanoid robots with different sizes in different environments. Afterward, we tested the well-known cascade classifier in combination with several image descriptors like Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set. Among the feature sets, Haar-like has the highest accuracy, LBP the highest recall, and HOG the highest precision. Considering Inter-Robot Interaction, it is evident that false positives are less troublesome than false negatives, thus LBP is more useful than the others.

AB - Robot Interaction has always been a challenge in collaborative robotics. In tasks comprising Inter-Robot Interaction, robot detection is very often needed. We explore humanoid robots detection because, humanoid robots can be useful in many scenarios, and everything from helping elderly people live in their own homes to responding to disasters. Cameras are chosen because they are reach and cheap sensors, and there are lots of mature two-dimensional (2D) and 3D computer vision libraries which facilitate Image analysis. To tackle humanoid robot detection effectively, we collected a data set of various humanoid robots with different sizes in different environments. Afterward, we tested the well-known cascade classifier in combination with several image descriptors like Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set. Among the feature sets, Haar-like has the highest accuracy, LBP the highest recall, and HOG the highest precision. Considering Inter-Robot Interaction, it is evident that false positives are less troublesome than false negatives, thus LBP is more useful than the others.